Abstract
Recent sentiment analysis studies have demonstrated that many services such as public opinion surveys and reputation analyses are derived from a variety of documentary resources. The annotated corpus in sentiment analysis is one essential resource, as are other NLP technologies such as POS tagging and named entity extraction. The sentiment annotation policy should be defined according to the task and relevant document genre. Recently, many sentiment corpora have been published in news, review, and blog genres. However, a sentiment corpus in the dialog document genre, which involves questions and answers, has yet to be studied, and a sentiment annotation policy has yet to be clearly defined. In this paper, we explain an approach to annotating and creating a sentiment corpus with detailed sentiment types using community QA documents in BCCWJ. We also identify the different sentiment characteristics in a corpus through combinations of annotations to provide novel insights in the challenging topics of opinion question answering and domain adaptation.
